In comparisons between item-limit and continuous-resource models of working memory, the continuous-resource model tested is usually a stereotyped one in which memory resource is divided equally among items. This model cannot account for human behavior. We recently introduced the notion that resource (mnemonic precision) is variable across items and trials. This model provides excellent fits to data and outperforms item-limit models in explaining delayed-estimation data. When studying change detection, a model of memory is not enough, since the task contains a decision stage. Augmenting the variable-precision model of memory with a Bayesian decision model provides the best available account of change detection performance across set sizes and change magnitudes. Finally, we argue that variable, continuous precision has a plausible neural basis in the gain of a neural population. Our results and those of other groups overhaul long-held beliefs about the limitations of working memory.